Pho Bulous, a Vietnamese restaurant in the bustling metropolis of Edmond, has had great success using forecasting techniques to predict demand for their main menu items ever since they opened their doors

Question

Their forecast for last month was grossly inaccurate and so far this month, their forecast appears to be just as bad as last month's. It's already time to prepare the forecast for next month, what should they do about their model?

Answer 1

The answer depends on whether Pho Bulous believes that last month's and this month's results are aberrations or the start of something new. Both causal and time-series techniques assume that there has been no change in how the world works, that is, independent factors of time or other variables will permit the forecaster to make accurate predictions about the future. If Pho Bulous believes that there is a significant change in the system, for example, a new competitor in the Edmond restaurant scene, a significant change in population or in their disposable income, then they might try multiple regression to include these factors or weight more recent data more heavily in a time-series model (the scenario isn't specific about which technique they have used thus far). Pho Bulous might also try a combination approach if they feel their situation has changed significantly. On the other hand, if Pho Bulous feels that these two months are not reflective of any major paradigm shift for the restaurant crowd in Edmond, they could continue to use the model(s) they have had success with in the past.